WHU-Hi: UAV-borne hyperspectral and high spatial resolution (H2)|
benchmark datasets for crop precise classification
WHU-Hi dataset (Wuhan UAV-borne hyperspectral image) is collected and shared by the RSIDEA research group of Wuhan University, and it could serve as a benchmark dataset for precise crop classification and hyperspectral image classification studies. The WHU-Hi dataset contains three individual UAV-borne hyperspectral datasets: WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu. All the datasets were acquired in farming areas with various crop types in Hubei province, China, via a Headwall Nano-Hyperspec sensor mounted on a UAV platform. Compared with spaceborne and airborne hyperspectral platforms, unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution (which we refer to here as H2 imagery). The research was published in Remote Sensing of Environment.
1. WHU-Hi dataset
The WHU-Hi dataset preprocessing included radiometric calibration and geometric correction, which were undertaken in the HyperSpec software provided by the instrument manufacturer. For the radiometric calibration, the raw digital number values were converted into radiance values by the laboratory calibration parameters of the sensor.
1.1 WHU-Hi-LongKou dataset
The WHU-Hi-LongKou dataset was acquired from 13:49 to 14:37 on July 17, 2018, in Longkou Town, Hubei province, China, with an 8-mm focal length Headwall Nano-Hyperspec imaging sensor equipped on a DJI Matrice 600 Pro (DJI M600 Pro) UAV platform. During the data collection, the weather was clear and cloudless, the temperature was about 36°C, and the relative air humidity was about 65%. The study area is a simple agricultural scene, which contains six crop species: corn, cotton, sesame, broad-leaf soybean, narrow-leaf soybean, and rice. The UAV flew at an altitude of 500 m, the size of the imagery is 550 × 400 pixels, there are 270 bands from 400 to 1000 nm, and the spatial resolution of the UAV-borne hyperspectral imagery is about 0.463 m. An overview of this dataset is provided in Fig. 1 and Table. 1.
1.2 WHU-Hi-HanChuan dataset
The WHU-Hi-HanChuan dataset was acquired from 17:57 to 18:46 on June 17, 2016, in Hanchuan, Hubei province, China, with an 17-mm focal length Headwall Nano-Hyperspec imaging sensor equipped on a Leica Aibot X6 UAV V1 platform. During the data collection, the weather was clear and cloudless, the temperature was about 30°C, and the relative air humidity was about 70%. The study area is a rural-urban fringe zone with buildings, water, and cultivated land, which contains seven crop species: strawberry, cowpea, soybean, sorghum, water spinach, watermelon, and greens. The UAV flew at an altitude of 250 m, the size of the imagery is 1217 × 303 pixels, there are 274 bands from 400 to 1000 nm, and the spatial resolution of the UAV-borne hyperspectral imagery is about 0.109 m. Notably, since the WHU-Hi-HanChuan dataset was acquired during the afternoon when the solar elevation angle was low, there are many shadow-covered areas in the image. An overview of this dataset is given in Fig. 2 and Table. 2.
1.3 WHU-Hi-HongHu dataset
The WHU-Hi-HongHu dataset was acquired from 16:23 to 17:37 on November 20, 2017, in Honghu City, Hubei province, China, with a 17-mm focal length Headwall Nano-Hyperspec imaging sensor equipped on a DJI Matrice 600 Pro UAV platform. During the data collection, the weather was cloudy, the temperature was about 8°C, and the relative air humidity was about 55%. The experimental area is a complex agricultural scene with many classes of crops, and different cultivars of the same crop are also planted in the region, including Chinese cabbage and cabbage, and Brassica chinensis and small Brassica chinensis. Notably, the region is planted with different cultivars of the same crop type; for example, Chinese cabbage/cabbage and brassica chinensis/small brassica chinensis. The UAV flew at an altitude of 100 m, the size of the imagery is 940 × 475 pixels, there are 270 bands from 400 to 1000 nm, and the spatial resolution of the UAV-borne hyperspectral imagery is about 0.043 m. An overview of this dataset is provided in Fig. 3 and Table. 3.
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Table 4 shows the classification accuracy of some advanced hyperspectral classification methods on the WHU-Hi dataset.
1. C.-C. Chang, and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Trans Intell Syst Technol, vol. 2, no. 3, pp. 27, May. 2011.
2. M. Baatz, and A. Schäpe, “Multiresolution segmentation:an optimization approach for high quality multi-scale image segmentation,” 2000.
3. Y. Zhong, X. Lin, and L. Zhang, “A support vector conditional random fields classifier with a Mahalanobis distance boundary constraint for high spatial resolution remote sensing imagery,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1314-1330, Apr. 2014.
4. K. Makantasis, K. Karantzalos, A. Doulamis et al., “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” Proc. IEEE IGARSS, pp. 4959-4962, Jul. 2015.
5. Y. Chen, Z. Lin, X. Zhao et al., “Deep learning-based classification of hyperspectral data,” IEEE J Sel Top Appl Earth Observ Remote Sens, vol. 7, no. 6, pp. 2094-2107, Jun. 2014.
6. X. Mei, E. Pan, Y. Ma et al., “Spectral-Spatial Attention Networks for Hyperspectral Image Classification,” Remote Sens, vol. 11, no. 8, pp. 963, Apr. 2019.
7. Z. Zhong, J. Li, Z. Luo et al., “Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework,” IEEE Trans Geosci Remote Sens, vol. 56, no. 2, pp. 847-858, Feb. 2018.
8. M. E. Paoletti, J. M. Haut, R. Fernandez-Beltran, J. Plaza, A. J. Plaza and F. Pla, "Deep pyramidal residual networks for spectral-spatial hyperspectral image classification", IEEE Trans. Geosci. Remote Sens., vol. 57, no. 2, pp. 740-754, Feb. 2019.
9. Y. Xu, B. Du and L. Zhang, "Beyond the patchwise classification: Spectral–spatial fully convolutional networks for hyperspectral image classification", IEEE Trans. Big Data, Jun. 2020.
10. Z. Zheng, Y. Zhong, A. Ma and L. Zhang, "FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification," in IEEE Trans. Geosci. Remote Sens, vol. 58, no. 8, pp. 5612-5626, Aug. 2020, doi: 10.1109/TGRS.2020.2967821.
11. Zhong Y, Hu X, Luo C, et al. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF[J]. Remote Sens. Environ, 2020, 250: 112012.
The copyright belongs to Intelligent Data Extraction, Analysis and Applications of Remote Sensing(RSIDEA) academic research group, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University. The WHU-Hi dataset can be used for academic purposes only and need to cite the following paper, but any commercial use is prohibited. Otherwise, RSIDEA of Wuhan University reserves the right to pursue legal responsibility.
 Y. Zhong, X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang, “WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF”, Remote Sens. Environ., vol. 250, pp. 112012, 2020.
 Y. Zhong, X. Wang, Y. Xu, S. Wang, T. Jia, X. Hu, J. Zhao, L. Wei, and L. Zhang, "Mini-UAV-borne hyperspectral remote sensing: From observation and processing to applications", IEEE Geosci. Remote Sens. Mag., vol. 6, no. 4, pp. 46-62, Dec. 2018.
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Mr. Xin Hu: firstname.lastname@example.org
Dr. Xingyu Wang: email@example.com
Prof. Yanfei Zhong: firstname.lastname@example.org